Reversible Data Hiding With Optimal Encryption

Abstract:This project discussed about a brain controlled robot based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communica-tion and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a mo-bile robot can be controlled. The intention of the project work is to develop a robot that can assist the disabled people in their daily life to do some work independent on others. Brain signals will be sensed by the brain wave sen-sor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Matlab platform. Then the control commands will be transmitted to the robotic ARM module to process. With this entire system, we can pick an object and place it accordingly through the configured brain signals.

Key words: ARM, BCI,

I. Introduction:An EEG-based brain-controlled robot is a robot that uses EEG-based BCIs to receive human control (hereafter, brain controlled robots refer to EEG-based brain-con-trolled robots only). Two main classes of brain-controlled robots to assist disabilities are brain-controlled manipula-tors and mobile robots. One representative work of brain-controlled manipulators is the manipulator used within the FRIEND system developed by Graser which is able to show the brain controlled capabilities of robots out of a controlled laboratory situation. Brain-controlled mo-bile robots can be divided into two categories according to their operational modes. One category is called “direct control by the BCI,” which means that the BCI translates EEG signals into motion commands to control robots di-rectly.

G.SwathiPG Scholar,

Stanley College of Engineering Technology for Women, Hyderabad-500075.

M.Lalitha SowmyaAssistant Professor,

Stanley College of Engineering Technology for Women, Hyderabad-500075.

Various approaches to implement this method are shown in Table I. One typical example is the work of Tanaka et al. [21], who first developed a brain-controlled robotic wheelchair whose left or right turning movements are directly controlled by corresponding motion commands translated from user brain signals while imagining left or right limb movements, and tested this system in real-world situations. However, the overall performance of these brain-controlled mobile robots mainly depends on the performance of the noninvasive BCIs, which are cur-rently slow and uncertain. In other words, the performance of the BCI systems limits that of the robots. Further, users need to issue motor control commands rather frequently, often causing user fatigue. To address the two questions aforementioned that the robots directly controlled by a BCI meet, so as to make the user be able to control the ro-bot over a long period of time, the second group of brain-controlled robots has been developed from a perspective of shared control, where a user (using a BCI) and an intel-ligent controller (such as autonomous navigation system) share the control over the robots.Block Diagram:

Volume No: 2 (2017), Issue No: 8 (January) January 2017 www. IJRACSE.com Page 1

Reversible Data Hiding With Optimal Encryption

Fig1: BCI- Pick and Place

II. Design and Implementation:Electroencephalography (EEG) is the measurement of electrical activity in the living brain. In this project we used a brinwave sensor MW001 to analyse the EEG sig-nals . This design discuss about processing and record-ing the raw EEG signal from the MindWave sensor in the MATLAB environment and through Zigbee transmission control commands will be passed to the Robot section. Mindwave sensors are not used in clinical use, but are used in the Brain Control Interface (BCI) and neurofeed-back (one of biofeedback types). The BCI is a direct com-munication pathway between the brain and an external device.

Working Principle:The principle of operation is quite simple. Two dry sen-sors are used to detect and filter the EEG signals. The sensor tip detects electrical signals from the forehead of the brain. At the same time, the sensor pick up ambient noise generated by human muscle, computers, light bulbs, electrical sockets and other electrical devices. The second sensor, ear clip, is a grounds and reference, which allows thinkgear chip to filter out the electrical noise.The device measures the raw signal, power spectrum (alpha, beta, delta, gamma, theta), attention level, mediation level and blink detection. The raw EEG data received at a rate of 512 Hz. Other measured values are made every second. Therefore, raw EEG data is a main source of information on EEG signals using MindWave MW001.

The number of the serial port is given during the pairing of the device. The device can connect on modes 1200, 2400, 4800, 9600, 57600 and 115200 bits per second (bps). Here, we use the 9600 bps rate and stream 5V RAW mode, because these parameters have the minimum of transmission errors.

III. System Hardware:LPC2148 Processor:LPC2148 Microcontroller Architecture. The ARM7TD-MI-S is a general purpose 32-bit microprocessor, which offers high performance and very low power consump-tion. The ARM architecture is based on Reduced Instruc-tion Set Computer (RISC) principles, and the instruction set and related decode mechanism are much simpler than those of micro programmed Complex Instruction Set Computers (CISC). This simplicity results in a high in-struction throughput and impressive real-time interrupt response from a small and cost-effective processor core. Pipeline techniques are employed so that all parts of the processing and memory systems can operate continu-ously. Typically, while one instruction is being executed, its successor is being decoded, and a third instruction is being fetched from memory. The ARM7TDMI-S proces-sor also employs a unique architectural strategy known as Thumb, which makes it ideally suited to high-volume applications with memory restrictions, or applications where code density is an issue.

The key idea behind Thumb is that of a super-reduced instruction set. Essentially, the ARM7TDMI-S processor has two instruction sets:

•The standard 32-bit ARM set. •A 16-bit Thumb set.

The Thumb set’s 16-bit instruction length allows it to ap-proach twice the density of standard ARM code while re-taining most of the ARM’s performance advantage over a traditional 16-bit processor using 16-bit registers. This is possible because Thumb code operates on the same 32-bit register set as ARM code. Thumb code is able to provide up to 65% of the code size of ARM, and 160% of the per-formance of an equivalent ARM processor connected to a 16-bit memory system

• Standard 32-bit ARMv5TE set• 16-bit THUMB set

Design Theory:A.Matlab Platform:The MATLAB allows to include thinkgear.dll. This en-vironment has broad support in toolbox, which makes it ideal for a scientific research. This paper presents how recording and processing the raw EEG signal in MAT-LAB environment using MindWave sensor. The Com-munication Protocol, shows a system of digital rules for message exchange between MATLAB environment and MindWave MW001 device. This section also presents the main parameters of thinkgear library.

B.The Communications Protocol:The proposed communications protocol is a system of simple rules for message exchanges between MATLAB and the EEG device. It consists of 7 basic steps, which are presented in following steps.

Load ThinkGear library into MATLAB* Get a connection ID handle to ThinkGear* Attempt to connect the connection ID handle to serial *

port “COMx”Waiting to establish the connection* Read packets from the connection* Close the connection* Unload ThinkGear library*

In the first step are functions with parameters in the fol-lowing order::

1. libisloaded(‘Thinkgear’) – returns true if the Think-Gear library is loaded, and false otherwise.

2. loadlibrary(‘Thinkgear.dll’,’thinkgear.h’) – loads the functions defined in the header file and found in the li-brary. Now, the function calllib() can call a function in the ThinkGear library.

3. calllib(‘Thinkgear’, ‘TG_GetDriverVersion’) – returns the version of loaded library.

In the next step, the function calllib(‘Thinkgear’, ‘TG_GetNewConnectionId’) gets a new connection ID handle to ThinkGear. The value -1 is returned if too many con-nections have been created. In the ThinkGear library, the most important function is TG_Connect. This function needs 4 parameters: the connection ID, number of the se-rial port, Baud rate and type of data.

1.Brain wave sensor:Electroencephalography (EEG) is the measurement of electrical activity in the living brain. In this project we used a brinwave sensor MW001 to analyse the EEG sig-nals . This design discuss about processing and record-ing the raw EEG signal from the MindWave sensor in the MATLAB environment and through Zigbee transmission control commands will be passed to the Robot section. Mindwave sensors are not used in clinical use, but are used in the Brain Control Interface (BCI) and neurofeed-back (one of biofeedback types). The BCI is a direct com-munication pathway between the brain and an external device.

ThinkGear ASIC Module:• Directly connects to dry electrode (as opposed to con-ventional medical wet sensors)• One EEG channel with three contacts: EEG; REF; and GND• Improper fit detected through “Poor Signal Quality” warning from ASIC to reset if off the head for four con-secutive seconds, or if it is receiving a poor signal for seven consecutive seconds• Advanced filtering technology with high noise immu-nity• Low power consumption suitable for portable battery-driven applications• Max power consumption 15mA @ 3.3 V• Raw EEG data output at 512 bits per second

Think Gear or TGAM Features + Technical Specifica-tions

Measures:• Raw brainwave signal• Processing and output of EEG power spectrums (Alpha, Beta, etc.)• Processing and output of NeuroSky proprietary eSense meter for Attention, Meditation, and other future meters• EEG/ECG signal quality analysis (can be used to detect poor contact and whether the device is off the head)• Eyeblink detection

Electrodes:• Maximum surface area of ~150mm2 (but less surface area is optimal)• Ag/AgCl, Stainless Steel, Gold, or/and Silver (both solid and plated material works)



Fig1: BCI- Pick and Place

II. Design and Implementation:Electroencephalography (EEG) is the measurement of electrical activity in the living brain. In this project we used a brinwave sensor MW001 to analyse the EEG sig-nals . This design discuss about processing and record-ing the raw EEG signal from the MindWave sensor in the MATLAB environment and through Zigbee transmission control commands will be passed to the Robot section. Mindwave sensors are not used in clinical use, but are used in the Brain Control Interface (BCI) and neurofeed-back (one of biofeedback types). The BCI is a direct com-munication pathway between the brain and an external device.

Working Principle:The principle of operation is quite simple. Two dry sen-sors are used to detect and filter the EEG signals. The sensor tip detects electrical signals from the forehead of the brain. At the same time, the sensor pick up ambient noise generated by human muscle, computers, light bulbs, electrical sockets and other electrical devices. The second sensor, ear clip, is a grounds and reference, which allows thinkgear chip to filter out the electrical noise.The device measures the raw signal, power spectrum (alpha, beta, delta, gamma, theta), attention level, mediation level and blink detection. The raw EEG data received at a rate of 512 Hz. Other measured values are made every second. Therefore, raw EEG data is a main source of information on EEG signals using MindWave MW001.

The number of the serial port is given during the pairing of the device. The device can connect on modes 1200, 2400, 4800, 9600, 57600 and 115200 bits per second (bps). Here, we use the 9600 bps rate and stream 5V RAW mode, because these parameters have the minimum of transmission errors.

III. System Hardware:LPC2148 Processor:LPC2148 Microcontroller Architecture. The ARM7TD-MI-S is a general purpose 32-bit microprocessor, which offers high performance and very low power consump-tion. The ARM architecture is based on Reduced Instruc-tion Set Computer (RISC) principles, and the instruction set and related decode mechanism are much simpler than those of micro programmed Complex Instruction Set Computers (CISC). This simplicity results in a high in-struction throughput and impressive real-time interrupt response from a small and cost-effective processor core. Pipeline techniques are employed so that all parts of the processing and memory systems can operate continu-ously. Typically, while one instruction is being executed, its successor is being decoded, and a third instruction is being fetched from memory. The ARM7TDMI-S proces-sor also employs a unique architectural strategy known as Thumb, which makes it ideally suited to high-volume applications with memory restrictions, or applications where code density is an issue.

The key idea behind Thumb is that of a super-reduced instruction set. Essentially, the ARM7TDMI-S processor has two instruction sets:

•The standard 32-bit ARM set. •A 16-bit Thumb set.

The Thumb set’s 16-bit instruction length allows it to ap-proach twice the density of standard ARM code while re-taining most of the ARM’s performance advantage over a traditional 16-bit processor using 16-bit registers. This is possible because Thumb code operates on the same 32-bit register set as ARM code. Thumb code is able to provide up to 65% of the code size of ARM, and 160% of the per-formance of an equivalent ARM processor connected to a 16-bit memory system

• Standard 32-bit ARMv5TE set• 16-bit THUMB set

Design Theory:A.Matlab Platform:The MATLAB allows to include thinkgear.dll. This en-vironment has broad support in toolbox, which makes it ideal for a scientific research. This paper presents how recording and processing the raw EEG signal in MAT-LAB environment using MindWave sensor. The Com-munication Protocol, shows a system of digital rules for message exchange between MATLAB environment and MindWave MW001 device. This section also presents the main parameters of thinkgear library.

B.The Communications Protocol:The proposed communications protocol is a system of simple rules for message exchanges between MATLAB and the EEG device. It consists of 7 basic steps, which are presented in following steps.

Load ThinkGear library into MATLAB* Get a connection ID handle to ThinkGear* Attempt to connect the connection ID handle to serial *

port “COMx”Waiting to establish the connection* Read packets from the connection* Close the connection* Unload ThinkGear library*

In the first step are functions with parameters in the fol-lowing order::

1. libisloaded(‘Thinkgear’) – returns true if the Think-Gear library is loaded, and false otherwise.

2. loadlibrary(‘Thinkgear.dll’,’thinkgear.h’) – loads the functions defined in the header file and found in the li-brary. Now, the function calllib() can call a function in the ThinkGear library.

3. calllib(‘Thinkgear’, ‘TG_GetDriverVersion’) – returns the version of loaded library.

In the next step, the function calllib(‘Thinkgear’, ‘TG_GetNewConnectionId’) gets a new connection ID handle to ThinkGear. The value -1 is returned if too many con-nections have been created. In the ThinkGear library, the most important function is TG_Connect. This function needs 4 parameters: the connection ID, number of the se-rial port, Baud rate and type of data.

1.Brain wave sensor:Electroencephalography (EEG) is the measurement of electrical activity in the living brain. In this project we used a brinwave sensor MW001 to analyse the EEG sig-nals . This design discuss about processing and record-ing the raw EEG signal from the MindWave sensor in the MATLAB environment and through Zigbee transmission control commands will be passed to the Robot section. Mindwave sensors are not used in clinical use, but are used in the Brain Control Interface (BCI) and neurofeed-back (one of biofeedback types). The BCI is a direct com-munication pathway between the brain and an external device.

ThinkGear ASIC Module:• Directly connects to dry electrode (as opposed to con-ventional medical wet sensors)• One EEG channel with three contacts: EEG; REF; and GND• Improper fit detected through “Poor Signal Quality” warning from ASIC to reset if off the head for four con-secutive seconds, or if it is receiving a poor signal for seven consecutive seconds• Advanced filtering technology with high noise immu-nity• Low power consumption suitable for portable battery-driven applications• Max power consumption 15mA @ 3.3 V• Raw EEG data output at 512 bits per second

Think Gear or TGAM Features + Technical Specifica-tions

Measures:• Raw brainwave signal• Processing and output of EEG power spectrums (Alpha, Beta, etc.)• Processing and output of NeuroSky proprietary eSense meter for Attention, Meditation, and other future meters• EEG/ECG signal quality analysis (can be used to detect poor contact and whether the device is off the head)• Eyeblink detection

Electrodes:• Maximum surface area of ~150mm2 (but less surface area is optimal)• Ag/AgCl, Stainless Steel, Gold, or/and Silver (both solid and plated material works)



• EEG electrode located above the left or right eye on the forehead• Ground and reference electrodes located behind the ear or at the earlobe• Have enough pressure to prevent movement, with a min-imum of 0.8 PSI

2.L293D:L293D is a dual H-bridge motor driver integrated circuit (IC). Motor drivers act as current amplifiers since they take a low-current control signal and provide a higher-current signal. This higher current signal is used to drive the motors.L293D contains two inbuilt H-bridge driver circuits. In its common mode of operation, two DC mo-tors can be driven simultaneously, both in forward and reverse direction. The motor operations of two motors can be controlled by input logic at pins 2 & 7 and 10 & 15. Input logic 00 or 11 will stop the corresponding motor. Logic 01 and 10 will rotate it in clockwise and anticlock-wise directions, respectively.

DC motor:DC motors are configured in many types and sizes, in-cluding brush less, servo, and gear motor types. A motor consists of a rotor and a permanent magnetic field stator. The magnetic field is maintained using either permanent magnets or electromagnetic windings..Motors are the de-vices that provide the actual speed and torque in a drive system. This family includes AC motor types (single and multiphase motors, universal, servo motors, induction, synchronous, and gear motor) and DC motors (brush less, servo motor, and gear motor) as well as linear, stepper and air motors, and motor contactors and starters.

3.IEEE 802.15.4 Protocol:The XBee/XBee-PRO RF Modules are designed to op-erate within the ZigBee protocol and support the unique needs of low-cost, low-power wireless sensor networks. The modules require minimal power and provide reliable delivery of data between remote devices. The modules operate within the ISM 2.4 GHz frequency band and are compatible with the following.

Advanced Networking & Security »•Point-to-point topology•point-to-multipoint topology•Self-routing, self-healing and fault-tolerant •mesh networking

Low Power »• TX Current: 295 mA • RX Current: 45 mA Power-down Current: < 1 μA

IV. System Software:MATLAB’s Graphical User Interface Development Envi-ronment (GUIDE) provides a rich set of tools for incor-porating graphical user interfaces (GUIs) in M-functions. Using GUIDE, the processes of laying out a GUI (i.e., its buttons, pop-up menus, etc.)and programming the opera-tion of the GUI are divided conveniently into two easily managed and relatively independent tasks.

Role of Matlab in BCI:The MATLAB allows to include thinkgear.dll. This en-vironment has broad support in toolbox, which makes it ideal for a scientific research. This paper presents how recording and processing the raw EEG signal in MAT-LAB environment using MindWave sensor. The Com-munication Protocol, shows a system of digital rules for message exchange between MATLAB environment and MindWave MW001 device. This section also presents the main parameters of thinkgear library. The connection is established through command:calllib(‘Thinkgear’,’TG_Connect’,Id,ComPortName,TG_BAUD_115200,TG_ STREAM _5VRAW). In the next step, we must attempt to read a Packet of data from the connection. We use the TG_ReadPackets() function with ID parameter and number of packet to read. The command calllib(‘Thinkgear’,’TG_ReadPackets’, Id,1) returns false for error, and otherwise true. The function TG_GetValueStatus() checks if a value has been updated by TG_ReadPackets(). If this function returns true, we can use TG_GetValue() function to get the updated value of the raw EEG signal.

Fig: Brian signal representation in MATLAB

The above graph representation includes the attention val-ue and blink strength. Based on this signals Pick and


Place module will be controlled. Based on the brain signal commands the object will be picked and placed by the ARM module.

V. Conclusion:This project discussed about a brain controlled robot based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communica-tion and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands an ob-ject can be picked and placed in any environment.This project will become an assistive technology for disabled people in future.

VI. References:[1] N. Birbaumer, N. Ghanayim, T. Hinterberger, I. Ivers-en, B. Kotchoubey, A. Kubler, J. Perelmouter, E. Taub, and H. Flor, “A spelling device for the paralyzed,” Na-ture, vol. 398, pp. 297–298, Mar. 1999.

[2] K.-R. M¨uller and B. Blankertz, “Toward noninvasive brain–computer interfaces,” IEEE Signal Process. Mag., vol. 23, no. 5, pp. 125–128, Sep. 2006.

[3] J. Williamson, R. Murray-Smith, B. Blankertz, M. Krauledat, and K.-R. M¨uller, “Designing for uncertain, asymmetric control: Interaction design for brain–comput-er interfaces,” Int. J. Human-Comput. Stud., vol. 67, no. 10, pp. 827–841, Oct. 2009.

[4] Y. Li, H. Li, and C. Guan, “A self-training semi-super-vised SVM algorithm and its application in an EEG-based brain computer interface speller system,” Pattern Recog-nit. Lett., vol. 29, no. 9, pp. 1285–1294, 2008.

[5] Y. Su, B. Wu, W. Chen, J. Zhang, J. Jiang, Y. Zhuang, and X. Zheng, “P300-based brain computer interface: Prototype of a Chinese speller,” J. Comput. Inf. Syst., vol. 4, no. 4, pp. 1515–1522, 2008.

[6] B. Hong, F. Guo, T. Liu, X. Gao, and S.Gao, “N200-speller using motiononset visual response,” Clin. Neuro-physiol., vol. 120, no. 9, pp. 1658– 1666, Sep. 2009.

[7] A. A. Karim, T. Hinterberger, and J. Richter, “Neural internet: Web surfing with brain potentials for the com-pletely paralyzed,” Neurorehabil. Neural Repair, vol. 20, no. 4, pp. 508–515, 2006.



Reversible Data Hiding With Optimal Encryption

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